The present disclosure relates generally to the field of process control and/or optimization systems. More specifically, the present disclosure relates to systems for use in building and/or testing processes for the design and/or production of various products.
Optimization and modeling algorithms are used to optimize and model various environments. Some systems utilize biologically inspired algorithms (e.g., genetic algorithms, particle swarm intelligence, evolutionary strategies, etc.) to perform optimization and modeling due to their ability to properly learn and optimize variant environments rapidly. Genetic algorithms may be relatively slow and ineffective at handling environments which change during optimization. Such genetic algorithms may need to be restarted to effectively handle the changes upon detection of an environmental change, which may result in poor performance of the algorithm. Biologically inspired algorithms also are not equipped to maintain memory of previously encountered environments and must be restarted from scratch upon significant environmental change detection. This may result in poor performance and/or abandonment of the methodology for optimization and modeling.
One possible application of optimization and modeling methods is in the area of testing in product design and/or manufacturing processes. The testing of products throughout the manufacturing process results in a vast amount of data which is often analyzed in a post process model. Analysis of this data is typically limited to standard Six Sigma or statistical process control methodologies. This form of analysis is susceptible to overlooking correlations between differing test configurations as well as other non-intuitive relationships within the data itself. Often, this analysis is performed too late in the testing process to affect the most beneficial change. Post processing of test data requires that complete testing be performed even though failures may occur during the testing process.